Uncertainty Quantification in Portfolio Temperature Alignment

Paper: arXiv 2412.14182

Abstract

We present a novel Bayesian framework for quantifying uncertainty in portfolio temperature alignment models, leveraging the X-Degree Compatibility (XDC) approach with the scientifically validated Finite Amplitude Impulse Response (FaIR) climate model. This framework significantly advances the widely adopted linear approaches that use the Transient Climate Response to Cumulative CO2 Emissions (TCRE). Developed in collaboration with right°, one of the pioneering companies in portfolio temperature alignment, our methodology addresses key sources of uncertainty, including parameter variability and input emission data across diverse decarbonization pathways. By employing adaptive Markov Chain Monte Carlo (MCMC) methods, we provide robust parametric uncertainty quantification for the FaIR model. To enhance computational efficiency, we integrate a deep learning-based emulator, enabling near real-time simulations. Through practical examples, we demonstrate how this framework improves climate risk management and decision-making in portfolio construction by treating uncertainty as a critical feature rather than a constraint. Moreover, our approach identifies the primary sources of uncertainty, offering valuable insights for future research.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced statistical methods (Bayesian framework, adaptive MCMC) and complex climate modeling (FaIR model with deep learning emulation), indicating high mathematical density. It includes practical examples, collaboration with an industry partner (right°), and mentions GitHub code for reproducibility, demonstrating significant empirical rigor and backtest-ready implementation.

Research Flowchart

  flowchart TD
  A["Research Goal: Quantify Uncertainty<br>in Portfolio Temperature Alignment"] --> B["Key Methodology: Bayesian Framework<br>with FaIR Climate Model & XDC"]

  B --> C{"Input Data"}
  C --> C1["Portfolio Emission Data"]
  C --> C2["Decarbonization Pathways"]
  C --> C3["Climate Parameter Priors"]

  B --> D["Computational Process"]
  subgraph D ["Adaptive MCMC & Emulation"]
      D1["Parametric Uncertainty<br>via MCMC"]
      D2["Deep Learning Emulator<br>for Speed"]
  end

  C --> D1
  D1 --> D2

  D --> E["Key Findings & Outcomes"]
  E --> E1["Robust Uncertainty<br>Quantification"]
  E --> E2["Identification of<br>Uncertainty Sources"]
  E --> E3["Improved Climate Risk<br>Management in Portfolios"]

Cited References (58)

1. Uncertainties in the Emissions Database for Global Atmospheric Research (EDGAR) emission inventory of greenhouse gases (2021) — link
2. A New Scenario Framework for Climate Change Research: The Concept of Shared Socioeconomic Pathways (2014) — link
3. Net Zero by 2050 (2021)
4. Climate change 2021: The physical science basis (2021)
5. Climate Change 2014 Synthesis Report — link
6. Good practices for climate-related and environmental risk management (2022) — link
7. Walking the talk - Banks gearing up to manage risks from climate change – results of the 2022 thematic review on climate-related and environmental risks (2022) — link
8. European Banks’ corporate loan books - a pilot study on banks’ alignment with the temperature target of the P aris agreement (2024) — link

0 of 58 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.